A pipeline that translates sentences and entities, then matches entities by orthographic, phonetic, and distributional similarity, improves cross-lingual NER over prior projection baselines.
Reporting Score Distributions Makes a Difference: Performance Study of LSTM-networks for Sequence Tagging
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abstract
In this paper we show that reporting a single performance score is insufficient to compare non-deterministic approaches. We demonstrate for common sequence tagging tasks that the seed value for the random number generator can result in statistically significant (p < 10^-4) differences for state-of-the-art systems. For two recent systems for NER, we observe an absolute difference of one percentage point F1-score depending on the selected seed value, making these systems perceived either as state-of-the-art or mediocre. Instead of publishing and reporting single performance scores, we propose to compare score distributions based on multiple executions. Based on the evaluation of 50.000 LSTM-networks for five sequence tagging tasks, we present network architectures that produce both superior performance as well as are more stable with respect to the remaining hyperparameters.
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2019 1verdicts
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Entity Projection via Machine Translation for Cross-Lingual NER
A pipeline that translates sentences and entities, then matches entities by orthographic, phonetic, and distributional similarity, improves cross-lingual NER over prior projection baselines.